Wind-blown dust modeling using a backward Lagrangian particle dispersion model
Mallia, Derek V.; Kochanski, Adam; Wu, Dien; Pennell, Chris; Oswald, Whitney; Lin, John C.
2017-09-01 00:00:00
AbstractPresented here is a new dust modeling framework that uses a backward Lagrangian particle dispersion model coupled with a dust emission model, both driven by meteorological data from the Weather Research and Forecasting model (WRF). The modeling framework’s performance was tested during the spring of 2010 at multiple sites across northern Utah. Initial model results for March-April 2010 showed that the model was able to replicate the 27-28 April 2010 dust event; however, it was unable to reproduce a significant wind-blown dust event on 30 March 2010. During this event, the model significantly underestimated observed PM2.5 concentrations (4.7 μg m−3 vs. 38.7 μg m−3) along the Wasatch Front. The backward Lagrangian approach presented here allowed for the easy identification of dust source regions with misrepresented land cover and soil types, which required an update to WRF. In addition, changes were also applied to the dust emission model to account for dry lake basins. These updates significantly improved dust model simulations with modeled PM2.5 comparing much more favorably to observed PM2.5 concentrations (average of 30.3 μg m−3) in addition to better resolving the timing of the frontal passage. The dust model was also applied in a forecasting setting, with the model able to replicate the magnitude of a large dust event, albeit with a 2-hour lag. These results suggest that the dust modeling framework presented here has potential for replicating past dust events, identifying potential source regions of dust, and short-term forecasting applications.
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pngJournal of Applied Meteorology and ClimatologyAmerican Meteorological Societyhttp://www.deepdyve.com/lp/american-meteorological-society/wind-blown-dust-modeling-using-a-backward-lagrangian-particle-1B5g053AXs

Abstract

AbstractPresented here is a new dust modeling framework that uses a backward Lagrangian particle dispersion model coupled with a dust emission model, both driven by meteorological data from the Weather Research and Forecasting model (WRF). The modeling framework’s performance was tested during the spring of 2010 at multiple sites across northern Utah. Initial model results for March-April 2010 showed that the model was able to replicate the 27-28 April 2010 dust event; however, it was unable to reproduce a significant wind-blown dust event on 30 March 2010. During this event, the model significantly underestimated observed PM2.5 concentrations (4.7 μg m−3 vs. 38.7 μg m−3) along the Wasatch Front. The backward Lagrangian approach presented here allowed for the easy identification of dust source regions with misrepresented land cover and soil types, which required an update to WRF. In addition, changes were also applied to the dust emission model to account for dry lake basins. These updates significantly improved dust model simulations with modeled PM2.5 comparing much more favorably to observed PM2.5 concentrations (average of 30.3 μg m−3) in addition to better resolving the timing of the frontal passage. The dust model was also applied in a forecasting setting, with the model able to replicate the magnitude of a large dust event, albeit with a 2-hour lag. These results suggest that the dust modeling framework presented here has potential for replicating past dust events, identifying potential source regions of dust, and short-term forecasting applications.

Journal

Journal of Applied Meteorology and Climatology
– American Meteorological Society

Published: Sep 1, 2017

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References

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